Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen3
zen
zen-embedding
zenlm
hanzo
embedding
retrieval
text-embeddings-inference
Instructions to use zenlm/zen-embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zenlm/zen-embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zenlm/zen-embedding-0.6B") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Zen Embedding 0.6B
0.6B-parameter text-embedding model for retrieval-augmented generation, semantic search, and dense retrieval. Part of the Zen embedding line.
Repackaged from Qwen/Qwen3-Embedding-0.6B (apache-2.0, Alibaba Qwen). Native HuggingFace safetensors, re-hosted under the Zen embedding line. Not trained from scratch — a permissively-licensed redistribution for the OSS-clean Zen model line.
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("zenlm/zen-embedding-0.6B")
emb = model.encode(["The weather is lovely today.", "It's so sunny outside!"])
print(model.similarity(emb, emb))
Hosted via the Hanzo gateway (api.hanzo.ai) as zen-embedding-0.6b.
GGUF build for CPU inference: zenlm/zen-embedding-0.6B-GGUF.
License
apache-2.0. Upstream: Qwen/Qwen3-Embedding-0.6B by Alibaba Qwen. This repository redistributes the weights under the same license.
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